1 | % * This code was used in the following articles:
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2 | % * [1] Learning 3-D Scene Structure from a Single Still Image,
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3 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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4 | % * In ICCV workshop on 3D Representation for Recognition (3dRR-07), 2007.
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5 | % * (best paper)
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6 | % * [2] 3-D Reconstruction from Sparse Views using Monocular Vision,
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7 | % * Ashutosh Saxena, Min Sun, Andrew Y. Ng,
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8 | % * In ICCV workshop on Virtual Representations and Modeling
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9 | % * of Large-scale environments (VRML), 2007.
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10 | % * [3] 3-D Depth Reconstruction from a Single Still Image,
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11 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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12 | % * International Journal of Computer Vision (IJCV), Aug 2007.
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13 | % * [6] Learning Depth from Single Monocular Images,
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14 | % * Ashutosh Saxena, Sung H. Chung, Andrew Y. Ng.
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15 | % * In Neural Information Processing Systems (NIPS) 18, 2005.
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16 | % *
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17 | % * These articles are available at:
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18 | % * http://make3d.stanford.edu/publications
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19 | % *
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20 | % * We request that you cite the papers [1], [3] and [6] in any of
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21 | % * your reports that uses this code.
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22 | % * Further, if you use the code in image3dstiching/ (multiple image version),
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23 | % * then please cite [2].
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24 | % *
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25 | % * If you use the code in third_party/, then PLEASE CITE and follow the
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26 | % * LICENSE OF THE CORRESPONDING THIRD PARTY CODE.
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27 | % *
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28 | % * Finally, this code is for non-commercial use only. For further
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29 | % * information and to obtain a copy of the license, see
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30 | % *
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31 | % * http://make3d.stanford.edu/publications/code
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32 | % *
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33 | % * Also, the software distributed under the License is distributed on an
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34 | % * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
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35 | % * express or implied. See the License for the specific language governing
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36 | % * permissions and limitations under the License.
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37 | % *
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38 | % */
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39 | function [SupFact, nList] = AnalyzeSup(Sup,maskSky) |
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40 | |
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41 | % this function analyze the Sup of the NuPatch in each Sup index |
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42 | NuSup = sort( setdiff( unique(Sup)',0)); |
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43 | edges =[ NuSup]; |
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44 | [yn xn] = size(Sup); |
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45 | |
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46 | % 1 ) NuPatch in each Sup |
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47 | SupFact = []; %NuSup' histc( Sup(:), edges) |
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48 | nList = []; |
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49 | for i = NuSup |
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50 | mask = Sup == i; |
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51 | SupFactTemp = []; |
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52 | |
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53 | % 2) Center Position of the Sup |
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54 | [y x] = find(mask); |
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55 | y50 = prctile(y,50); |
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56 | x50 = prctile(x,50); |
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57 | SupFactTemp = [SupFactTemp y50/yn x50/xn ]; |
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58 | |
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59 | % 3) x^2 y^2 position of superpixel |
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60 | SupFactTemp = [SupFactTemp SupFactTemp(:,(end-1):end).^2 ]; |
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61 | |
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62 | % 4) x y 10th & 90th |
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63 | y90 = prctile(y,90); |
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64 | x90 = prctile(x,90); |
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65 | y10 = prctile(y,10); |
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66 | x10 = prctile(x,10); |
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67 | SupFactTemp = [SupFactTemp y90/yn x90/xn y10/yn x10/xn ]; |
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68 | |
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69 | % 5) eccentricity |
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70 | x = x/xn; |
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71 | y = y/yn; |
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72 | C = cov([x y]); |
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73 | [v e] = eig(C); |
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74 | tt = diag(e); |
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75 | if size(tt,1)~=2 |
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76 | tt = [tt ;tt]; |
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77 | end |
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78 | [I C] = max(abs(tt)); |
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79 | ta = v(:,C).*sign(tt(C)); |
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80 | SupFactTemp = [SupFactTemp sqrt(abs(tt))' acos(ta(1))']; |
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81 | |
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82 | % 6) Neighbor count |
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83 | SE = strel('disk',3); |
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84 | mask_dilate = imdilate(mask,SE); |
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85 | mask_dilate_edge = mask_dilate; |
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86 | mask_dilate_edge(mask) = 0; |
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87 | mask_dilate_edge(maskSky) = 0; |
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88 | target =unique(Sup(mask_dilate_edge)); |
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89 | if any(target <= 0) || any(isnan(target)) |
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90 | disp('AnalyzeSup error'); |
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91 | end |
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92 | newNei = [i*ones(size(target,1),1) target]; |
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93 | newNei = sort(newNei,2); |
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94 | nList = [ nList; newNei]; |
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95 | SupFactTemp = [SupFactTemp size(target,1)]; |
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96 | |
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97 | SupFact = [SupFact; SupFactTemp]; |
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98 | end |
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99 | nList = unique(nList,'rows'); |
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100 | SupFact = [NuSup' histc( Sup(:), edges) SupFact]; |
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101 | return; |
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